基于LSTM的北极海冰范围多步预测策略研究

作者：王漫漫1 2 3 4  邹斌2 3  石立坚2 3  曾韬2 3  张颖2 3  路敦旺1 2 3

单位：1. 国家海洋环境预报中心, 北京 100081; 2. 国家卫星海洋应用中心, 北京 100081; 3. 自然资源部 空间海洋遥感与应用研究重点实验室, 北京 100081; 4. 国家海洋环境预报中心 自然资源部海洋灾害预报技术重点实验室, 北京 100081

关键词：北极海冰范围 长短期记忆网络 多步预测策略 Seq2Seq策略

分类号：P731.32

出版年·卷·期（页码）：2025·42·第一期（11-22）

摘要：

已有研究对北极海冰范围开展单步预测，而多步预测及其策略研究有待进一步探索。使用1979—2022年的北极月平均海冰范围数据，采用长短期记忆网络（LSTM）深度学习方法，结合递归（Recursive）、直接（Direct）、多输入多输出（MIMO）和Seq2Seq策略实现对未来12个月北极海冰范围的多步预测。结果表明：24个月为模型的最佳输入长度；与另外3种基本的多步预测策略相比，Seq2Seq策略对12个月北极海冰范围预测的准确性更好，均方根误差为3.30×105 km2。

While previous researches have primarily focused on single-step prediction of Arctic sea ice extent, multi-step prediction and strategy are yet to be explored. This study utilizes monthly average Arctic sea ice extent data spanning from 1978 to 2022 and employs Long Short-Term Memory to implement multi-step predictions of Arctic sea ice extent for the next 12 months using four strategies: Recursive, Direct, Multi-input Multi-output, and Seq2Seq. The results show that a model input length of 24 months performs optimally. When compared to the other three basic multi-step prediction strategies, the Seq2Seq strategy demonstrates superior accuracy in forecasting Arctic sea ice extent over the next 12 months, with an root mean square error of 0.33 million square kilometers.

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